A better understanding of tumor subsets that benefit from treatment is of critical importance to enable personalized medicine. For this reason, a number of molecular approaches to classify cancer and identify responsive subsets are now being tested. Proteomic strategies (which examine global patterns of protein expression or phosphorylation) are also being used to identify subsets of tumors. This includes classic immunohistochemistry approaches to measure protein expression in paraffin fixed tumor sections, use of phospho-specific antibodies to measure specific phosphorylation events on particular proteins, proteomic profiling tools (such as reverse phase protein arrays), and mass spectrometry based approaches. Biomarker systems to measure protein-protein interaction biomarkers in cancer have lagged behind these other tools. This is an important missing component of most biomarker strategies, as cellular signaling requires proper formation of signaling complexes and networks of proteins that act in concert to produce a physiological signal.
It is known that oncoproteins, such as epidermal growth factor receptor (EGFR) or V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS), produce a signaling network comprised of a defined set of molecules that lead to “oncogene addiction” and cell transformation. A better understanding of the oncoprotein signaling network could uncover novel therapeutic targets or therapeutic strategies and allow “network medicine” to become a reality.